Integrating microgrids into the existing power system increases utility grid reliability and efficiency. Optimal sizing and configuration of Microgrid (MG) components are essential at the design stage for minimizing the overall cost and emissions of the MG. This paper presents a novel mathematical model for optimizing the configuration and operation of a grid-connected microgrid exploiting Demand-Side Management (DSM). This paper studies the impact of Demand Response (DR)-based DSM on optimal MG design from the customers' and utility's perspectives, considering both economic and technical performance metrics. A comparative performance study of six DR programs is investigated in order to verify the effectiveness of the proposed Renewable Generation-based Dynamic Pricing (RGDP)-based DR. In this paper, the sizing problem of MG is formulated as a bi-objective optimization in order to fulfil two objective functions: minimizing the total annual cost and minimizing life cycle emissions. An advanced evolutionary algorithm named the Dandelion Algorithm is utilized in order to solve the complex and constrained nonlinear optimization problem. The results demonstrate that considering RGDP-based DR in the MG sizing problem reduces the installation and operating costs of the MG without load shedding. The proposed DR strategy satisfies the customers' needs with high efficiency and low cost.